通过基于损伤区域的多类学习,提高基于人工智能的间歇性肺病检测的可解释性
Asumi Yamazaki1, Ryo Yamazaki2, Munetaka Kita2
1Department of Radiological Technology, Faculty of Health Sciences, Kobe Tokiwa University, Nagata-ku, Kobe, Hyogo, 653-0838, Japan.
Journal of imaging informatics in medicine
|January 20, 2026
概括
这项研究增强了深度学习 (DL) 模型,用于检测间歇性肺病 (ILD),使用四类分类方案. 这种方法提高了模型透明度和诊断准确性,用于在胸部X射线图上识别ILD.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 肺部医学 肺部医学
背景情况:
- 从胸部放射图中诊断间歇性肺病 (ILD) 是一个挑战.
- 深度学习 (DL) 对ILD检测有希望,但缺乏透明度.
- DL模型的可解释性对于临床信任和决策至关重要.
研究的目的:
- 开发一个可靠和强大的ILD检测系统,使用四类分类方案.
- 评估卷积神经网络 (CNN) 和基于变压器的DL模型对ILD的解释性.
- 通过提高透明度,提高DL在ILD诊断中的临床实用性.
主要方法:
- 开发了用于ILD检测的四类分类方案.
- 评估了CNN和变压器模型的可解释性,使用交叉与联盟 (IoU) 和子系数.
- 在五种可视化方法中,量化热图与实际损伤区域的一致性.
主要成果:
- 四类方案显著增加了四种可视化方法的CNN模型的IoU和Dice系数.
- 变压器模型显示,通过特定的可视化方法和四类方案,改进了 IoU 和 Dice 系数.
- 实现了0.356的最高平均IOU和0.483的子系数,证明了更好的解释性.
结论:
- 多类分类利用病变区域信息提高了ILD检测的DL模型可解释性.
- 选择适当的可视化方法对于特定的DL模型至关重要.
- 结果支持改善临床决策,以便更早,更可靠地管理ILD.
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